Performance Analysis of KVM Hypervisor Using a Self-Driving Developer Kit

Thilo Muller, Hadi Askaripoor, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Virtualization plays an increasingly important role in safety-critical embedded systems like modern vehicles due to its numerous advantages like cost reduction, better scalability, or easier safety verification. These advantages come at the cost of additional performance overhead. While performance evaluation of virtualization solutions for server applications has been the subject of research for a long time, it is still not adequately investigated for embedded use cases. We present the first work to analyze the performance of KVM, an open-source hypervisor, on an Nvidia Drive AGX, a developer kit for self-driving vehicles. Experimental measurements show that the computational overhead restricts the number of partitions. Also, poor memory and file I/O performance suggest that KVM is not usable for practical applications with the Nvidia Drive AGX.

Original languageEnglish
Title of host publicationIECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9781665480253
DOIs
StatePublished - 2022
Event48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022 - Brussels, Belgium
Duration: 17 Oct 202220 Oct 2022

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2022-October
ISSN (Print)2162-4704
ISSN (Electronic)2577-1647

Conference

Conference48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022
Country/TerritoryBelgium
CityBrussels
Period17/10/2220/10/22

Keywords

  • Autonomous Driving
  • Benchmarking
  • Hypervisor
  • KVM
  • Nvidia Drive AGX
  • Performance Evaluation
  • Safety-critical Systems
  • Virtualization

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